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Model-based material decomposition is a statisticaliterative reconstructionframework where basis material densityimages are estimated directly fromspectral CT data. This methoduses a physical model for polyenergetic x-raytransmission andattenuation and therefore it does not typically sufferfrombeam-hardening artifacts. However, this estimation is a poorly-conditionedinverse problem due to the strong anticorrelationbetween basis materials. Inthis work we propose an precondi-tioned optimization algorithm for a nonlinearpenalized weightedleast-squares objective function.

A Preconditioned Algorithm for Model-Based Iterative CT Reconstruction and Material Decomposition from Spectral CT Data